Technical Field
The present invention generally relates to crop type identification, and more particularly, to crop type identification based on satellite observation and weather data.
Description of the Related Art
The United States Department of Agriculture (USDA) publishes crop acreage estimates, which include the acreages of different crop types that have been planted in a given county, crop district, state, and or the entire country. Reliable information about crop acreage, however, is typically available from the USDA only after the growing season has concluded. The estimates provide valuable insight into several factors for subsequent growing seasons, including estimated supply and demand of crop types for the following year. Farmers, for example, may utilize such estimates to determine efficient farm management plans at the earliest possible time, include what crop types to plant, how much acreage to dedicate to certain crop types, and what seeds and fertilizer to purchase.
The dominant approach towards early estimate crop acreage utilizes satellite remote sensing alone. Different crop types change color differently throughout their growth season. For example, corn fields typically turns green earlier than soybean fields in late spring. Using a set of satellite images captured during different dates of a growing season, one may observe how the color of each of the pixels on the satellite images varies over the growing season. Using the color variation, one may determine the pixel corresponding to what type of crop being planted to perform crop type identification. Then for a given county, crop district, state, or country, one may sum up the areas of all pixels corresponding to specific crop types to estimate the total acreage crop for the crops.
However, the current approach for crop type identification using satellite images by themselves is not very accurate. The main reason for the inaccuracy is that year-to-year variations in planting dates, as well as year-to-year variations in crop growth during the growing season, both related to variations in the yearly and local weather, makes the same type of crop grow at different rates. For example in one year, on a certain day of the year, the corn crop may already have reached the flowering stage, while in another year at the same day of the year, the corn crop is still two weeks away from the flowering stage. Consequently, the color variation of the same type of crop as a function of dates during the year, which is measured by satellite during the growth season, behaves differently in different years and at different locations. Thus, a crop type identification model may have difficulty in accurately differentiating crop types using satellite images alone.